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478 results for “3D Data”
Functional and ecomorphological evolution of orbit shape in Mesozoic archosaurs is driven by body size and diet: Geometric morphometric data, 3D models (stl files), FEA models (Hypermesh, Abaqus files)
<p class="MsoNormal">The orbit is one of several skull openings in the archosauromorph skull. Intuitively, it could be assumed that orbit shape would closely approximate the shape and size of the eyeball resulting in a predominantly circular morphology. However, a quantification of orbit shape across Archosauromorpha using a geometric morphometric approach demonstrates a large morphological diversity despite the fact that the majority of species retained a circular orbit. This morphological diversity is nearly exclusively driven by large (skull length > 1000 mm) and carnivorous species in all studied archosauromorph groups, but particularly prominently in theropod dinosaurs. While circular orbit shapes are retained in most herbivores and smaller species, as well as in juveniles and early ontogenetic stages, large carnivores adopted elliptical and keyhole-shaped orbits. Biomechanical modeling using finite element analysis reveals that these morphologies are beneficial in mitigating and dissipating feeding-induced stresses without additional reinforcement of the bony structure of the skull.</p>
Data set from a 3D benchmark simulation of delamination in a layered composite component under fatigue loading
<p>This data repository provides the processed data obtained from the benchmark simulation using the method presented in the article entitled "A simulation method for fatigue-driven delamination in layered structures involving non-negligible fracture process zones and arbitrarily shaped crack fronts" by the same authors. The method is based on a cohesive zone model approach and a Paris' law-based expression describing the crack growth rate. The capabilities of the method are evaluated by comparison of the results obtained from a benchmark test on a partially reinforced double cantilever beam (DCB) specimen with varying crack growth rate and front shape. The experimental work is presented in the article entitled "A benchmark test for validating 3D simulation methods for delamination growth under quasi-static and fatigue loading". Experimental data is provided in the Mendeley data set entitled "Experimental data set from a benchmark test of delamination growth with varying crack growth rate and crack front shape under quasi-static and fatigue loading". This data set only contains the simulation results. The test configuration, the methodology and the data processing are addressed in the related articles.</p>
Data for "Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit"
<h2>1. Repository Information</h2> <p>This repository contains the data produced during the work discussed in in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad7c14" target="_blank" rel="noopener">Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit</a>". Please refer to this paper for more details on how the data was produced.</p> <p> </p> <h2>2. Citing</h2> <p>In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citation is:</p> <p>Hanno Sahlmann and Waleed Sherif 2024 <em>Class. Quantum Grav.</em> <strong>41</strong> 215006</p> <p> </p> <h2>3. File Description</h2> <p>In this repository, you will find 4 general directories (here called parent directories):</p> <ol> <li>Ground Energy + Fluctuations</li> <li>Misc</li> <li>Quantum Constraint</li> <li>Volume</li> </ol> <p>Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Ground Energy + Fluctuations" contains the data used in Table 1 and Table 2 in the paper while the "Volume" directory contains the data used in Section 4.3 of the paper).</p> <p>Some of these parent directories, which involve simulations solving constraints, contain within them several sub-directories (child directories) corresponding to different produced data. The raw data of the simulation can be found in a <code>.json</code> file inside the child directories.</p> <p> </p> <h2>4. Usage</h2> <h3>4.1 Raw Simulation Data</h3> <p>The <code>.json</code> files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:</p> <p><code>import json</code></p> <p><code>filePath = ...</code></p> <p><code>data = json.load(open(filePath))</code></p> <p>where <code>filePath</code> should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python <code>dict</code>.</p> <p> </p> <p>The dictionary will have <em>at least one</em> parent key called "Energy". The data in the "Energy" key corresponds to the data being minimised. The data in any other parent key correspond to operators which were being observed during the simulation. For example, in the data in the "Quantum Constraint" directory, some .json files will have multiple parent keys such as FG, H, HG, .... Each of these keys correspond to different operators which were observed during that simulation. Each parent key is yet another dictionary in itself. The structure of the dictionaries corresponding to any parent key are always the same and always include the keys:</p> <ul> <li>iters</li> <li>Mean</li> <li>Variance</li> <li>Sigma</li> <li>R_hat</li> <li>TauCorr</li> </ul> <p>Hence, to access the "Mean" values, you use <code>data["Energy"]["Mean"]</code> (or alternatively <code>data["FG"]["Mean"]</code> if you wish to observe the value of the F + G operator during the simulation). The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The <code>iters</code> array includes merely the iteration number. The <code>Mean</code> array includes the value of the expectation value of the constraint at the corresponding iteration. The <code>Variance</code>, <code>Sigma</code>, <code>R_hat</code> and <code>TauCorr</code> includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. </p> <p> </p> <h3>4.2 Variational State Data</h3> <p><em><strong>The files for the variational arrays are too large to be uploaded to a general repository hosting service. Therefore, they will be provided directly upon request in a direct download link. Please contact the author of the paper (Waleed Sherif, email: waleed.sherif@fau.de) for accessing the data. </strong></em></p> <p> </p> <h3>4.3 Fluctuation results</h3> <p>In some child directories, there will be a <code>.txt</code> file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.</p> <p> </p> <h2>4.4 Probabilities</h2> <p>The "Misc/Probabilities" directory contains <code>.npy</code> files which should be handled in the same manner as the variational states. These files correspond to the probability simulations conducted in section 4.4.4 in the paper.</p> <p> </p> <h2>5. Contact</h2> <p>Shall you have any unanswered questions regarding the usage of the data, please contact the author:</p> <p>Waleed Sherif</p> <p>email: waleed.sherif@fau.de</p> <p> </p> <h2>6. References</h2> <p>The data provided in this repository was produced using the <a href="https://github.com/netket" target="_blank" rel="noopener">NetKet</a>[1] package</p> <p>[1] <a href="https://doi.org/10.21468/SciPostPhysCodeb.7" target="_blank" rel="noopener">doi: 10.21468/SciPostPhysCodeb.7</a></p>
Raw data for 'Light-Activated 3D Printed Fish-Like Actuator'
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Raw data for "3D Quantum Anomalous Hall Effect in Magnetic Topological Insulator Trilayers of Hundred-Nanometer Thickness"
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Research Data - Gyroid ferromagnetic nanostructures in 3D magnonics
<p>Source data from micromagnetic simulations performed in COMSOL Multiphysics software and Python codes for data post-processing utilized in the paper "Gyroid ferromagnetic nanostructures in 3D magnonics."</p> <p>The files from Comsol (.mph) are without simulation solutions due to their large size - please contact me if needed.</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 5
<p>Supplementary videos including raw data</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 4
<p>The data set was analyzed in supplementary figure 3.</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 3
<p>The data set was analyzed in supplementary figure 2.</p>
Improved 3D Characterization of in-situ Soil Desiccation Cracking by multi-source Data Integration
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Data for "Stochastic Green's Function Method Considering Non-uniform Rise Time Distribution to Simulate 3D Broadband Ground-Motion"
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Data for Figures in "Dynamics of K$_2$Ni$_2$(SO$_4$)$_3$ governed by proximity to a 3D spin liquid model"
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The Magnetotelluric data, 3D resistivity model, and the reprocessed Seismic data for Timmins, Canada
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SuperDARN convection data and the 3D PPMLR-MHD simulation data on 27 February 2014
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Data repository for Uniaxial compression of 3D printed samples with voids: laboratory measurements compared with Effective Medium Theory
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Supporting data for Liquid fragmentation induced by particle aggregation during two-phase flow in 3D porous media
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GoF 3D test case data
<p>This is a more complete version of the dataset. Use this one.</p>
microSAM - 3d user study data
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Data from: A 3D anatomical atlas of appendage musculature in the chelicerate arthropod Limulus polyphemus
Limulus polyphemus, an archetypal chelicerate taxon, has interested both biological and paleontological researchers due to its unique suite of anatomical features and as a useful modern analogue for fossil arthropod groups. To assist the study and documentation of this iconic taxon, we present a 3D atlas on the appendage musculature, with specific focus on the muscles of the cephalothoracic appendages. As L. polyphemus appendage musculature has been the focus of extensive study, depicting the muscles in 3D will facilitate a more complete understanding thereof for future researchers. A large museum specimen was CT scanned to illustrate the major exoskeletal features of L. polyphemus. Micro-CT scans of iodine-stained appendages from fresh, non-museum specimens were digitally dissected to interactively depict appendage sections and muscles. This study has revealed the presence of two new muscles: one within the pushing leg, located dorsally relative to all other patella muscles, and the other within the male pedipalp, located in the modified tibiotarsus. This atlas increases accessibility to important internal and external morphological features of L. polyphemus and reduces the need for destructive fresh tissue dissection of specimens. Scanning, digitally dissecting, and documenting taxa in 3D is a pivotal step towards creating permanent digital records of life on Earth.
Data from: The tendinopathic Achilles tendon does not remain iso-volumetric upon repeated loading: insights from 3D ultrasound
Mid-portion Achilles tendinopathy (MAT) alters the normal three-dimensional (3D) morphology of the Achilles tendon (AT) at rest and under a single tensile load. However, how MAT changes the 3D morphology of the AT during repeated loading remains unclear. This study compared the AT longitudinal, transverse and volume strains during repeated loading of the tendinopathic AT with those of the contralateral tendon in people with unilateral MAT. Ten adults with unilateral MAT performed 10 successive 25 s submaximal (50%) voluntary isometric plantarflexion contractions with both legs. Freehand 3D ultrasound scans were recorded and used to measure whole AT, free AT and proximal AT longitudinal strains and free AT cross-sectional area (CSA) and volume strains. The free AT experienced higher longitudinal and CSA strain and reached steady state following a greater number of contractions (five contractions) in the tendinopathic AT compared with the contralateral tendon (three contractions). Further, free tendon CSA and volume strain were greater in the tendinopathic AT than in the contralateral tendon from the first contraction, whereas free AT longitudinal strain was not greater than that of the contralateral tendon until the fourth contraction. Volume loss from the tendon core therefore preceded the greater longitudinal strain in the tendinopathic AT. Overall, these findings suggest that the tendinopathic free AT experiences an exaggerated longitudinal and transverse strain response under repeated loading that is underpinned by an altered interaction between solid and fluid tendon matrix components. These alterations are indicative of accentuated poroelasticity and an altered local stress–strain environment within the tendinopathic free tendon matrix, which could affect tendon remodelling via mechanobiological pathways.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.